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Internet of Things (IoT) is a network which connects several devices with the internet to have quick and real-time information transmission, attaining intelligent management. IoT is still in its beginning so it faces a lot of challenges. One huge challenge related to data management is the detection of collective anomalies. A collective anomaly is a set of points which differ as a whole from the rest of the data points. However, the individual points are not considered anomalies. Detection of collective anomalies is of huge importance as collective anomalies usually denote the occurrence of events or certain incidents that should be detected as soon as possible before they cause damage to the surrounding environment. Detection of collective anomalies is considered a complicated process as it involves more than one point rather than only one point as in point anomaly detection process. As a result, in this paper, we propose a novel approach of collective anomaly detection using a variable sized window in IoT. Two techniques were used to detect the collective anomalies, namely: K-Means clustering and isolation forests. The proposed approach was applied on a machine temperature dataset of size around 22,000 and the experiments showed promising results.

Online Communities for Question Answering (CQA) such as Quora and Stack Overflow face the challenge of providing sufficient answers for the questions asked by users. The exponential growing rate of the unanswered questions compromises the effectiveness of the CQA frameworks as knowledge sharing platforms. The main reason for this issue is the inefficient routing of the questions to the potential answerers, who are the field experts and interested users. This paper proposes the deep-learning-based technique QR-DSSM to increase the accuracy of the question routing process. This technique uses deep semantic similarity model (DSSM) to extract semantic similarity features using deep neural networks and use the features to rank users' profiles. QR-DSSM maps the asked questions and the profiles of the users into a latent semantic space where the ability to answer is measured using the cosine similarity between the questions and the profiles of the users. QR-DSSM experiments outperformed the baseline models such as LDA, SVM, and Rank-SVM techniques and achieved an MRR score of 0.1737.

We introduce a new technique for fast computation of structural join "pattern trees" in XML. Using a small amount of pre-computed path information, typically small enough to fit easily in main memory, we are able to render structural join computation almost independent of data set size. Our technique is amenable to bit-mapped processing, leading to further speed-up. In this paper, we present our technique and experimentally evaluate its performance.